Holistic analysis of multidimensional sensor data for substrate processing equipment

ABSTRACT

A method includes receiving, by a processing device, first data. The first data includes data from one or more sensors of a processing chamber and is associated with a processing operation. The first data is resolved in at least two dimensions, one of which is time. The method further includes providing the first data to a model. The method further includes receiving from the model second data. The second data includes an indication of an evolution of a processing parameter during the processing operation. The method further includes causing performance of a corrective action in view of the second data.

TECHNICAL FIELD

The instant specification relates to analysis of multidimensional sensordata associated with substrate processing. More specifically, theinstant specification relates to holistic temporal analysis ofmultidimensional sensor data to generate an indication of evolution ofsubstrate processing parameters through a duration of a processingoperation.

BACKGROUND

Chambers are used in many types of processing systems. Examples ofchambers include etch chambers, deposition chambers, anneal chambers,and the like. Typically, a substrate, such as a semiconductor wafer, isplaced on a substrate support within the chamber and conditions in thechamber are set and maintained to process the substrate. Detailedunderstanding of processing conditions, the effect of conditions on asubstrate, and evolutions of these parameters over time enables tightcontrol of product properties.

SUMMARY

The following is a simplified summary of the disclosure in order toprovide a basic understanding of some aspects of the disclosure. Thissummary is not an extensive overview of the disclosure. It is intendedto neither identify key or critical elements of the disclosure, nordelineate any scope of the particular implementations of the disclosureor any scope of the claims. Its sole purpose is to present some conceptsof the disclosure in a simplified form as a prelude to the more detaileddescription that is presented later.

In one aspect of the disclosure, a method includes receiving, by aprocessing device, first data. The first data includes data from one ormore sensors of a processing chamber and is associated with a processingoperation. The first data is resolved in at least two dimensions, one ofwhich is time. The method further includes providing the first data to amodel. The method further includes receiving from the model second data.The second data includes an indication of an evolution of a processingparameter during the processing operation. The method further includescausing performance of a corrective action in view of the second data.

In another aspect of the disclosure, a system includes memory and aprocessing device, coupled to the memory. The processing device isconfigured to receive first data. The first data includes data from oneor more sensors of a processing chamber and is associated with aprocessing operation. The first data is resolved in at least twodimensions, one of which is time. The processing device is furtherconfigured to provide the first data to a model. The processing deviceis further configured to receive from the model second data. The seconddata includes an indication of an evolution of a processing parameterduring the processing operation. The method further includes causingperformance of a corrective action in view of the second data.

In another aspect of the disclosure, a non-transitory machine-readablestorage medium stores instructions which, when executed, cause aprocessing device to perform operations. The operations includereceiving first data. The first data includes data from one or moresensors of a processing chamber and is associated with a processingoperation. The first data is resolved in at least two dimensions, one ofwhich is time. The operations further include providing the first datato a model. The operations further include receiving from the modelsecond data. The second data includes an indication of an evolution of aprocessing parameter during the processing operation. The operationsfurther include causing performance of a corrective action in view ofthe second data.

BRIEF DESCRIPTION OF THE DRAWINGS

The present disclosure is illustrated by way of example, and not by wayof limitation in the figures of the accompanying drawings.

FIG. 1 is a block diagram illustrating an exemplary system (exemplarysystem architecture), according to some embodiments.

FIG. 2 is a block diagram of an example data set generator used tocreate data sets for a model, according to some embodiments.

FIG. 3 is a block diagram illustrating a system for generating outputdata, according to some embodiments

FIGS. 4A-C are flow diagrams of methods associated with analysis ofmultidimensional sensor data, according to some embodiments.

FIG. 5 depicts a data analysis system for utilizing multidimensionalsensor data to generate predictive data, according to some embodiments.

FIG. 6 is a block diagram illustrating a computer system, according tosome embodiments.

DETAILED DESCRIPTION

Described herein are technologies directed to processing ofmulti-dimensional time-dependent sensor data associated with one or moreprocessing operations performed in a processing chamber.Multi-dimensional time-dependent sensor data (e.g., data points includeat least time information, information indicative of a secondindependent variable, and a value) may be addressed holistically toenable understanding of the evolution of process parameters during theprocessing operations.

Manufacturing equipment (e.g., a processing chamber) is used to producesubstrates, such as semiconductor wafers. The properties of thesesubstrates are determined by the conditions in which the substrates wereprocessed. As substrate processing progresses, conditions in the chamberand/or the response of the substrate to those conditions may evolve. Forexample, a processing operation may include a substrate etchingoperation, e.g., an operation that removes material from the substrate.Processing parameters such as etch rate may change over the duration ofthe processing operation. Accurate understanding of temporal processparameter evolution may be used to predict properties of finishedproducts, improve process learning, improve process recipe generationand refining, improve consistency of substrates produced, optimizesubstrate production, etc.

In some cases, a substrate is placed within a chamber for processing.The processing chamber may include various sensors to report onconditions associated with processing the substrate, e.g., pressure andtemperature sensors may report on chamber conditions, sensors may detectspontaneous plasma emission and report on conditions of the plasma,reflectance or scatter of waves from the substrate may report onevolving substrate geometry, etc. Any of these or other sensorsassociated with a processing chamber may take multiple measurements intime over the duration of a processing operation.

In some conventional systems, data from one or more sensors collected atthe same time (or near the same time, e.g., analyzed as though the datawas collected simultaneously) may be treated together to generate anindication of conditions at that time. For example, data from multiplepressure sensors may be analyzed to determine a snapshot of pressureconditions in a chamber, optical reflectance data of a substrate may beprocessed to determine a snapshot of substrate surface geometry, etc.

In some systems, data may be processed in a time-independent manner,e.g., conclusions may be drawn from the data without including dataseparated in time in analysis, data may be analyzed on a frame-by-framebasis, etc. In some embodiments, further conclusions may be drawn byconcatenating an arrangement of these analysis in order, e.g., byfitting some analysis result drawn from several instances oftime-independent data with respect to time to draw a temporalconclusion. In some embodiments, time-independent analysis and/orconcatenated time-independent analysis may suffer from high noiselevels, e.g., due to limited collection time during an ongoingprocessing operation. Time-independent and/or concatenatedtime-independent analysis may be difficult to utilize if several piecesof information are targeted for analysis, e.g., a single frame of datamay include a limited number of data points, and the breadth conclusionsdrawn from one frame of data may be restricted by the volume ofavailable information in the data frame.

In one or more embodiments, methods and devices of the currentdisclosure may address at least one or more of these deficiencies of theconventional approach. This disclosure enables a method of treatingmultidimensional sensor data in a holistic fashion. Multidimensionalsensor data may be received from a processing chamber. Multidimensionaldata in this context indicates data resolved in more than oneindependent variable, wherein one of the independent variables is time.For example, spectral data associated with a processing operation may beresolved in wavelength and time, acoustic data may be resolved infrequency and time, pressure and temperature data may be resolved insensor number and time, etc. In some embodiments, data points resolvedin at least two dimensions are provided to a model. The model mayinclude a physics-based model, a machine learning model, etc. The modelmay be configured to treat the multi-dimensional data simultaneously,e.g., fit the temporal evolution of data along an orthogonal axissimultaneously.

Aspects of this disclosure result in technological advantages overconventional solutions. In a time-independent analysis, sparsity of dataconstrains the amount of information that may be inferred throughfitting, e.g., constrained to fewer floating parameters than availabledata points in a single time-independent analysis frame. Temporalanalysis (e.g., analysis of multidimensional data including timedependence holistically) may alleviate these restrictions: depending onthe sampling rate of sensors, length of the processing operation, etc.,the number of data points available in a temporal analysis ofmultidimensional sensor data may be many times larger than in a singleframe, e.g., ten times larger, one hundred times larger, etc. Moreparameters may be understood through such an analysis (e.g., moreinformation may be available for analysis by virtue of including datapoints associated with separate times in a fitting procedure, and thusmore information may be recovered by fitting the data than is availableby analysis of a single temporal frame).

In some systems, multidimensional data may include data collected atasynchronous sampling rates and/or asynchronous sampling time points.For example, spectral data may be separated into a number of wavelengthmeasurements (e.g., two dimensions of the multi-dimensional sensor datamay be time and wavelength). In some systems, data associated withdifferent wavelengths may be collected at different times, e.g., aspectrometer may collect data associated with a first wavelength at afirst time, a second wavelength at a second time, etc. In someembodiments, the spectral data may repeat spectral measurements, e.g.,spectral data associated with each wavelength may be collected multipletimes, for example by cycling through the target wavelengths multipletimes. A conventional frame-by-frame (e.g., time-independent) analysismay approximate each cycle of data to be associated with a single timepoint. A holistic time-dependent analysis may mitigate inaccuraciesassociated with such approximations by treating data points in amulti-dimensional manner (e.g., associating each spectral measurementwith a unique time point, in some embodiments).

In some embodiments, time-independent analysis may be subject to highlevels of noise. The number of data points included in a singletime-independent analysis operation (e.g., a single data frame) may besmall. Noise may be difficult to compensate for within a small number ofavailable data points, e.g., outlier detection may be difficult,smoothing of data may be difficult, etc. Temporal analysis ofmultidimensional sensor data may alleviate this noise sensitivity, byanalyzing a larger number of data points together, noise in individualdata points may be more easily handled and/or data points more easilyrejected by the analysis model.

Temporal holistic analysis of multidimensional sensor data may provide adetailed picture of temporal evolution of in-chamber parametersotherwise difficult to determine. For example, time-independent analysesmay report on etch depth at each time step, and etch rate inferred bytracking the temporal evolution of the etch depth over the duration of aprocessing operation. Temporal analysis may report directly on both etchrate and etch depth, e.g., by utilizing a physics-based model thatcharacterizes temporal evolution of optical reflectance data from asubstrate as etch depth increases. Increased precision, accuracy, andefficiency of process learning (e.g., understanding newly designedprocessing recipes, updating processing recipes for new applications,etc.), calibration of behavior-based process modeling, additionalinformation relevant for process control, greater condition, parameter,and product consistency, improved chamber matching, improved performanceevaluation, improved performance regulation (e.g., return of a tool toproduction after a maintenance event) etc., may all be enabled bytemporal analysis of multidimensional sensor data.

In one aspect of the disclosure, a method includes receiving, by aprocessing device, first data. The first data includes data from one ormore sensors of a processing chamber and is associated with a processingoperation. The first data is resolved in at least two dimensions, one ofwhich is time. The method further includes providing the first data to amodel. The method further includes receiving from the model second data.The second data includes an indication of an evolution of a processingparameter during the processing operation. The method further includescausing performance of a corrective action in view of the second data.

In another aspect of the disclosure, a system includes memory and aprocessing device, coupled to the memory. The processing device isconfigured to receive first data. The first data includes data from oneor more sensors of a processing chamber and is associated with aprocessing operation. The first data is resolved in at least twodimensions, one of which is time. The processing device is furtherconfigured to provide the first data to a model. The processing deviceis further configured to receive from the model second data. The seconddata includes an indication of an evolution of a processing parameterduring the processing operation. The method further includes causingperformance of a corrective action in view of the second data.

In another aspect of the disclosure, a non-transitory machine-readablestorage medium stores instructions which, when executed, cause aprocessing device to perform operations. The operations includereceiving first data. The first data includes data from one or moresensors of a processing chamber and is associated with a processingoperation. The first data is resolved in at least two dimensions, one ofwhich is time. The operations further include providing the first datato a model. The operations further include receiving from the modelsecond data. The second data includes an indication of an evolution of aprocessing parameter during the processing operation. The operationsfurther include causing performance of a corrective action in view ofthe second data.

FIG. 1 is a block diagram illustrating an exemplary system 100(exemplary system architecture), according to some embodiments. Thesystem 100 includes a client device 120, manufacturing equipment 124,sensors 126, metrology equipment 128, a predictive server 112, and datastore 140. Predictive server 112 may be part of predictive system 110.Predictive system 110 may further include server machines 170 and 180.

In some embodiments, manufacturing equipment 124 (e.g., cluster tool) ispart of a substrate processing system (e.g., integrated processingsystem). Manufacturing equipment 124 includes one or more of acontroller, an enclosure system (e.g., substrate carrier, front openingunified pod (FOUP), autoteach FOUP, process kit enclosure system,substrate enclosure system, cassette, etc.), a side storage pod (SSP),an aligner device (e.g., aligner chamber), a factory interface (e.g.,equipment front end module (EFEM)), a load lock, a transfer chamber, oneor more processing chambers, a robot arm (e.g., disposed in the transferchamber, disposed in the front interface, etc.), and/or the like. Theenclosure system, SSP, and load lock mount to the factory interface anda robot arm disposed in the factory interface is to transfer content(e.g., substrates, process kit rings, carriers, validation wafer, etc.)between the enclosure system, SSP, load lock, and factory interface. Thealigner device is disposed in the factory interface to align thecontent. The load lock and the processing chambers mount to the transferchamber and a robot arm disposed in the transfer chamber is to transfercontent (e.g., substrates, process kit rings, carriers, validationwafers, etc.) between the load lock, the processing chambers, and thetransfer chamber. In some embodiments, manufacturing equipment 124includes components of substrate processing systems. In someembodiments, manufacturing equipment 124 is used to produce one or moreproducts (e.g., substrates, semiconductors, wafers, etc.). In someembodiments, manufacturing equipment 124 is used to produce one or morecomponents to be used in substrate processing systems.

Sensors 126 may provide sensor data 142 associated with manufacturingequipment 124 (e.g., associated with producing, by manufacturingequipment 124, corresponding products, such as wafers). Sensor data 142may be used for equipment health and/or product health (e.g., productquality), for example. Manufacturing equipment 124 may produce productsfollowing a recipe or performing processing operations and/or processingruns over a period of time. In some embodiments, sensor data 142 mayinclude values of one or more of temperature (e.g., heater temperature),spacing (SP), pressure, High Frequency Radio Frequency (HFRF), voltageof Electrostatic Chuck (ESC), electrical current, flow (e.g., of one ormore gases), power, voltage, optical data (e.g., substrate reflectancespectra), acoustic data (e.g., substrate acoustic scattering data), etc.Sensor data may include in-situ measurements of a substrate in aprocessing chamber, e.g., a substrate undergoing processing operations.Sensor data 142 may include historical sensor data 144 and currentsensor data 146. Current data, as used herein, indicates data associatedwith a processing run in progress or a processing run currently underanalytic investigation, e.g., by providing sensor data, metrology data,manufacturing parameters, etc., to a machine learning or physics-basedmodel. Manufacturing equipment 124 may be configured according tomanufacturing parameters 150. Manufacturing parameters 150 may beassociated with or indicative of parameters such as hardware parameters(e.g., settings or components (e.g., size, type, etc.) of themanufacturing equipment 124) and/or process parameters of themanufacturing equipment. Manufacturing parameters 150 may includehistorical manufacturing data and/or current manufacturing data.Manufacturing parameters 150 may be indicative of input settings to themanufacturing device (e.g., heater power, gas flow, etc.). Sensor data142 and/or manufacturing parameters 150 may be generated while themanufacturing equipment 124 is performing manufacturing processes (e.g.,equipment readings when processing products). Sensor data 142 may bedifferent for each product (e.g., each wafer).

In some embodiments, sensor data 142, metrology data 160, and/ormanufacturing parameters 150 may be processed (e.g., by the clientdevice 120 and/or by the predictive server 112). Processing of sensordata 142 may include generating features. In some embodiments, thefeatures are a pattern in the sensor data 142, metrology data 160,and/or manufacturing parameters 150 (e.g., slope, width, height, peak,etc.) or a combination of values from the sensor data 142, metrologydata 160, and/or manufacturing parameters 150 (e.g., power derived fromvoltage and current, etc.). Sensor data 142 may include features and thefeatures may be used by predictive component 114 for performing signalprocessing and/or for obtaining predictive data 168, possibly forperformance of a corrective action. Predictive data 168 may be any dataassociated with predictive system 110, e.g. predicted performance dataof a substrate, of a substrate processing operation, of a component ofmanufacturing equipment 124, etc. In some embodiments, predictive data168 may be indicative of substrate metrology. In some embodiments,predictive data 168 may be indicative of process conditions. In someembodiments, predictive data 168 may be indicative of temporal evolutionof conditions, substrate metrology, processing rate, etc., during theduration of a process operation.

Each instance (e.g., set) of sensor data 142 may correspond to a product(e.g., a wafer), a set of manufacturing equipment, a type of substrateproduced by manufacturing equipment, a combination thereof, or the like.Each instance of metrology data 160 and manufacturing parameters 150 maylikewise correspond to a product, a set of manufacturing equipment, atype of substrate produced by manufacturing equipment, a combinationthereof, or the like. Data store 140 may further store informationassociating sets of different data types, e.g. information indicativethat a set of sensor data, a set of metrology data, and/or a set ofmanufacturing data are all associated with the same product,manufacturing equipment, type of substrate, etc. In some embodiments,predictive system 110 may generate predictive data 168 using machinelearning. In some embodiments, predictive system 110 may generatepredictive data 168 with the use of one or more physics-based models.

Client device 120, manufacturing equipment 124, sensors 126, metrologyequipment 128, predictive server 112, data store 140, server machine170, and server machine 180 may be coupled to each other via a network130. In some embodiments, network 130 is a public network that providesclient device 120 with access to predictive server 112, data store 140,and/or other publicly available computing devices. In some embodiments,network 130 is a private network that provides client device 120 accessto manufacturing equipment 124, sensors 126, metrology equipment 128,data store 140, and/or other privately available computing devices.Network 130 may include one or more Wide Area Networks (WANs), LocalArea Networks (LANs), wired networks (e.g., Ethernet network), wirelessnetworks (e.g., an 802.11 network or a Wi-Fi network), cellular networks(e.g., a Long Term Evolution (LTE) network), routers, hubs, switches,server computers, cloud computing networks, and/or a combinationthereof.

Client device 120 may include one or more computing devices such asPersonal Computers (PCs), laptops, mobile phones, smart phones, tabletcomputers, netbook computers, network connected televisions (“smartTV”), network-connected media players (e.g., Blu-ray player), aset-top-box, Over-the-Top (OTT) streaming devices, operator boxes, etc.Client device 120 may include one or more virtual computing devices,e.g., cloud-based computing devices, cloud computing services, etc.Client device 120 may include a corrective action component 122.Corrective action component 122 may receive user input (e.g., via aGraphical User Interface (GUI) displayed via the client device 120) ofan indication associated with manufacturing equipment 124. The userinterface may present an indication of evolution of a processingparameters, may present an indication of a corrective action to beperformed, etc. In some embodiments, corrective action component 122transmits the indication to the predictive system 110, receives output(e.g., predictive data 168) from predictive system 110, determines acorrective action based on the output, and causes the corrective actionto be implemented.

In some embodiments, predictive system 110 may further include apredictive component 114. Predictive component 114 may take dataretrieved from model 190 to generate predictive data 168. In someembodiments, predictive component 114 provides predictive data 168 toclient device 120, and client device 120 causes a corrective action viacorrective action component 122 in view of predictive data 168. In someembodiments, corrective action component 122 may receive current sensordata 146 associated with production of a substrate and provide the datato predictive system 110.

In some embodiments, corrective action component 122 stores data (e.g.,data associated with intermediate analysis steps in generatingpredictive data 168) in data store 140 and predictive server 112retrieves the data from data store 140. In some embodiments, predictiveserver 112 may store output (e.g., predictive data 168) of the trainedmodel(s) 190 in data store 140 and client device 120 may retrieve theoutput from data store 140. In some embodiments, corrective actioncomponent 122 receives an indication of a corrective action frompredictive system 110 and causes the corrective action to beimplemented. Each client device 120 may include an operating system thatallows users to one or more of generate, view, or edit data (e.g.,indication associated with manufacturing equipment 124, correctiveactions associated with manufacturing equipment 124, etc.).

In some embodiments, metrology data 160 corresponds to historicalproperty data of products (e.g., produced using manufacturing parametersassociated with historical sensor data and historical manufacturingparameters) and predictive data 168 is associated with predictedproperty data (e.g., of products to be produced or that have beenproduced in conditions recorded by current sensor data and/or currentmanufacturing parameters). In some embodiments, the predictive data 168is predicted metrology data (e.g., virtual metrology data) of theproducts to be produced or that have been produced according toconditions recorded as current sensor data and/or current manufacturingparameters. In some embodiments, predictive data 168 is or includes anindication of abnormalities (e.g., abnormal products, abnormalcomponents, abnormal manufacturing equipment, abnormal energy usage,etc.) and/or one or more causes of the abnormalities. In someembodiments, predictive data 168 includes an indication of change overtime or drift in some component of manufacturing equipment 124, sensors126, metrology equipment 128, and the like. In some embodiments,predictive data 168 includes an indication of an end of life of acomponent of manufacturing equipment 124, sensors 126, metrologyequipment 128, or the like. In some embodiments, predictive data 168includes a comparison of performance of a chamber, tool, recipe, productdesign, etc., to another.

Performing manufacturing processes that result in defective products canbe costly in time, energy, products, components, manufacturing equipment124, the cost of identifying the defects and discarding the defectiveproduct, the cost of discovering and correcting the cause of the defect,etc. By inputting sensor data 142 (e.g., current sensor data 146) into apredictive model (e.g., model 190), receiving output of predictive data168, and performing a corrective action based on predictive data 168,system 100 can have the technical advantage of avoiding the cost ofproducing, identifying, and discarding defective products.

Performing manufacturing processes that result in failure of thecomponents of the manufacturing equipment 124 can be costly in downtime,damage to products, damage to equipment, express ordering replacementcomponents, etc. By inputting sensor data 142 (e.g., current sensor data146) to a predictive model (e.g., model 190), receiving output ofpredictive data 168, comparing data over time to diagnose drifting orfailing components (e.g., also recorded as predictive data 168), andperforming corrective actions (e.g., predicted operational maintenance,such as replacement, processing, cleaning, etc. of components, updatingrecipe parameters, etc.) based on the predictive data 168, system 100can have the technical advantage of avoiding the cost of one or more ofunexpected component failure, unscheduled downtime, productivity loss,unexpected equipment failure, product scrap, or the like. Monitoring theperformance over time of components, e.g. manufacturing equipment 124,sensors 126, metrology equipment 128, and the like, may provideindications of degrading components. Monitoring the performance of acomponent over time may extend the component's operational lifetime, forinstance if, after a standard replacement interval passes, measurementsindicative that the component may still perform well (e.g., performanceabove a threshold) for a time (e.g., until the next planned maintenanceevent).

Manufacturing parameters may be suboptimal for producing products whichmay have costly results of increased resource (e.g., energy, coolant,gases, etc.) consumption, increased amount of time to produce theproducts, increased component failure, increased amounts of defectiveproducts, etc. By inputting the sensor data 142 into a trainedpredictive model (e.g., model 190), receiving an output of predictivedata 168, and performing (e.g., based on predictive data 168) acorrective action of updating manufacturing parameters (e.g., settingoptimal manufacturing parameters), system 100 can have the technicaladvantage of using optimal manufacturing parameters (e.g., hardwareparameters, process parameters, optimal design) to avoid costly resultsof suboptimal manufacturing parameters.

Corrective action may be associated with one or more of ComputationalProcess Control (CPC), Statistical Process Control (SPC) (e.g., SPC onelectronic components to determine process in control, SPC to predictuseful lifespan of components, SPC to compare to a graph of 3-sigma,etc.), Advanced Process Control (APC), model-based process control,preventative operative maintenance, design optimization, updating ofmanufacturing parameters, updating manufacturing recipes, feedbackcontrol, machine learning modification, or the like.

In some embodiments, the corrective action includes providing an alert(e.g., an alarm to stop or not perform the manufacturing process ifpredictive data 168 indicates a predicted abnormality, such as anabnormality of the product, a component, or manufacturing equipment 124)to a user. In some embodiments, performance of the corrective actionincludes causing updates to one or more manufacturing parameters. Insome embodiments, performance of the corrective action includes causingupdates to one or more calibration tables and/or equipment constants(e.g., a set point provided to a component may be adjusted by a valueacross a number of process recipes, for example voltage applied to aheater may be increased by 3% for all processes using the heater). Insome embodiments, performance of the correction action includes updatinga process recipe (e.g., to adjust an extent or rate of a processingparameter, such as etch rate, including horizontal etch rate, verticaletch rate, etc.; etch depth; deposition rate; deposition depth, etc.).

Manufacturing parameters may include hardware parameters (e.g.,replacing components, using certain components, replacing a processingchip, updating firmware, etc.) and/or process parameters (e.g.,temperature, pressure, flow, rate, electrical current, voltage, gasflow, lift speed, etc.). In some embodiments, the corrective actionincludes causing preventative operative maintenance (e.g., replace,process, clean, etc. components of the manufacturing equipment 124). Insome embodiments, the corrective action includes causing designoptimization (e.g., updating manufacturing parameters, manufacturingprocesses, manufacturing equipment 124, etc. for an optimized product).In some embodiments, the corrective action includes a updating a recipe(e.g., updating timing of manufacturing equipment 124 to be in an idlemode, a sleep mode, a warm-up mode, etc., updating set points such astemperature or pressure during a processing operation, etc.).

Predictive server 112, server machine 170, and server machine 180 mayeach include one or more computing devices such as a rackmount server, arouter computer, a server computer, a personal computer, a mainframecomputer, a laptop computer, a tablet computer, a desktop computer,Graphics Processing Unit (GPU), accelerator Application-SpecificIntegrated Circuit (ASIC) (e.g., Tensor Processing Unit (TPU)), etc.Predictive server 112, server machine 170, and server machine 180 mayinclude one or more virtual computing devices, e.g., cloud computingdevices, cloud computing services, remote computing resources, etc.

Predictive server 112 may include predictive component 114. Predictivecomponent 114 may be used to generate predictive data 168. In someembodiments, predictive component 114 may receive sensor data 142,and/or manufacturing parameters 150 (e.g., receive from the clientdevice 120, retrieve from the data store 140) and generate output, e.g.,predictive output, output for performing corrective actions associatedwith manufacturing equipment 124, etc., based on the provided data. Insome embodiments, predictive component 114 may use one or more models190 to determine the output for performing the corrective action basedon current data. Model 190 may be a single model, an ensemble model, ora collection of models used to process data. Model 190 may include oneor more physics-based models, digital twin models, supervised machinelearning models, unsupervised machine learning models, semi-supervisedmachine learning models, statistical models, etc.

In some embodiments, data indicative of properties of a substrate (e.g.,current sensor data 146) is provided to a modeling system including oneor more physics-based models. The modeling system may be configured togenerate an indication of the temporal evolution of one or moreprocesses (e.g., processing parameters), e.g., process time dependence,such as etch depth evolution, etch rate evolution, deposition rateevolution, etc. Current sensor data 146 provided to the modeling system(e.g., model 190 of predictive system 110) may be multidimensional,e.g., resolved in at least two independent variables, two independentaxes, etc. The multidimensional data may be resolved in time and atleast one other dimension. For example, the multidimensional data mayinclude optical reflectance data of a substrate in processing. The datamay include data from several wavelengths (e.g., a first dimension ofresolution may be wavelength) taken multiple times throughout a duration(e.g., a second dimensions of resolution of the data may be time).Possible dimensions of resolution may include wavelength (e.g., ofelectromagnetic radiation, including optical, IR, UV, X-Ray, etc.,analysis), frequency (e.g., of acoustic signals), location (e.g.,location of a sensor, location of spatially resolved data of asubstrate, etc.), sensor ID, etc.

The modeling system (e.g., model 190) may perform analysis operations(e.g., fitting to a physical model, generating output via a machinelearning model, etc.) upon the multidimensional data. In someembodiments, multidimensional sensor data (e.g., current sensor data146) may be provided to the modeling system after conclusion of aprocessing operation, e.g., sensor data indicative of the entireduration of an operation may be analyzed. In some embodiments,multidimensional sensor data associated with a portion of the durationof a processing operation may be analyzed. Multidimensional sensor datamay be analyzed holistically, e.g., data along multiple axes may be fitsimultaneously, in contrast to some systems that treat sensor data as aseries of frame-by-frame (e.g., time independent) analysis, stitchedtogether (e.g., plotted on a time axis) to generate an indication oftime evolution of a process. In some embodiments, a fit to amulti-dimensional physical model may be performed, e.g., a physicalunderstanding of the evolution of multiple measureable parameters overtime may be utilized to generate the physics-based model, generate afit, generate parameters for modeling process variable evolution, etc.

Holistic treatment of multidimensional sensor data (e.g., fitting dataresolved in multiple dimensions together) provides distinct advantagesto other methods. In some embodiments, sensor data may be sparse, e.g.,spectral data may be resolved into a fairly small number of wavelengthmeasurements (for example, to reduce measurement time so as to not delayprocessing operations). Fitting sparse data limits the amount ofinformation extractable from the analysis, e.g., due to overfitting.Sparse data is also more sensitive to noise than data sets including alarger number of data points. Holistic treatment of multidimensionalsensor data alleviates at least these challenges by utilizing datapoints collected at multiple times in a fitting/analysis scheme. Moredata points included in the fit increases the volume of extractableinformation (e.g., the number of dimensions of a product that may bepredicted, the accuracy to which the temporal evolution of a process orprocessing parameter may be predicted, etc.), increases reliability ofinformation extraction, increases resistance of analysis to noisysignals, etc.

In some embodiments, the modeling system (e.g., model 190) may include aphysics-based model. The physics-based model may provide a correlationbetween sensor data and a physical system (e.g., substrate). Thephysics-based model may be a time-dependent model, e.g., may describethe evolution of properties of the physical system (e.g., substrate)over time. For example, the physics-based model may describe theevolution of spectral reflectance data as etch depth increases over theduration of a processing operation. In some embodiments, thephysics-based model may accept time-resolved data associated withspectrally-resolved detection of electromagnetic radiation. Thephysics-based model may represent a process behavior parameter (e.g.,etch depth, etch rate, deposition rate, etc.) by a function (e.g., alinear function, a piece-wise function, a polynomial function,combinations of functions, etc.). The process behavior parameter may beexpressed as a function of time and a number of fit parameters (e.g.,polynomial coefficients) that are derived by the physics-based modelbased on the multi-dimensional input sensor data. In some embodiments,the modeling system may include a trained machine learning model. Themachine learning model may be configured to accept as input sensor dataand generate as output an indication of evolution of processingparameters. In some embodiments, a machine learning model may beconfigured to perform a subset of operations involved in the modelingsystem. Further description of a modeling system for analysis ofmultidimensional sensor data is found in connection with FIG. 5 .

In some embodiments, data indicative of properties of a substrateproduced (e.g., current sensor data 146) is provided to a trainedmachine learning model (e.g., model 190). The machine learning model istrained to output data indicative of a corrective action to produce asubstrate with different characteristics. In some embodiments, dataindicative of evolution of a processing parameter (e.g., etch depth,etch rate, etc.) is output by the machine learning model. In someembodiments, data indicative of a corrective action to adjust evolutionof a processing parameter is output by the machine learning model (e.g.,a recipe adjustment).

Historical sensor data may be used in combination with current sensordata to detect drift, changes, aging, etc. of components ofmanufacturing equipment 124. Sensor data 142 monitored over time maygenerate information indicative of changes to a processing system, e.g.,component drift or failure, sensor drift or failure, maintenance to beperformed, recovery of a chamber after maintenance is performed, etc.Predictive component 114 may use combinations and comparisons of sensordata 142, manufacture parameters 150, metrology data 160, etc. togenerate predictive data 168. In some embodiments, predictive data 168includes data predicting the lifetime of components of manufacturingequipment 124, sensors 126, etc.

In some embodiments, sensor data from a number of chambers may be usedto detect chamber operational differences, perform chamber matchingprocedures, etc. Sensor data 142 generated by multiple chambers may beprovided to modeling system, e.g., model 190. Model 190 may generate anindication of temporal processing parameter evolution over the durationof a processing operation in multiple processing chambers. Differencesis processing parameter evolution between chambers may indicate chambermatching procedures to be performed, e.g., recipe update, maintenance,component replacement, etc.

In some embodiments, predictive component 114 receives data, such assensor data 142, metrology data 160, manufacturing parameters 150, etc.,and may perform pre-processing such as extracting patterns in the dataor combining data to new composite data. Predictive component 114 maythen provide the data to model 190 as input. Model 190 may include oneor more physics-based models, digital twin models, machine learningmodels, etc., and may accept as input sensor data. Model 190 may includea trained machine learning model, a statistical model, etc., configuredto further process data associated with properties of a substratesupport. Predictive component 114 may receive from model 190 predictivedata, indicative of chamber performance, predicted substrate properties,a manufacturing fault, component drift, or the like. Predictivecomponent 114 may then cause a corrective action to occur. Thecorrective action may include sending an alert to client device 120. Thecorrective action may also include updating manufacturing parameters ofmanufacturing equipment 124. The corrective action may also includegenerating predictive data 168, indicative of chamber or instrumentdrift, aging, or failure, recipe success or failure, predicted productproperties, etc.

Data store 140 may be a memory (e.g., random access memory), a drive(e.g., a hard drive, a flash drive), a database system, or another typeof component or device capable of storing data. Data store 140 mayinclude multiple storage components (e.g., multiple drives or multipledatabases) that may span multiple computing devices (e.g., multipleserver computers). Data store 140 may include remote storage, cloud datastorage, cloud-based storage services, etc. Data store 140 may storesensor data 142, manufacturing parameters 150, metrology data 160, andpredictive data 168. Sensor data 142 may include sensor data time tracesover the duration of manufacturing processes, associations of data withphysical sensors, pre-processed data, such as averages and compositedata, and data indicative of sensor performance over time (i.e., manymanufacturing processes). Sensor data 142 may include multidimensionaldata, e.g., data resolved in both time and at least one other dimension.Manufacturing parameters 150 and metrology data 160 may contain similarfeatures, e.g., pre-processed data, associations between data andproducts/operations, etc. Sensor data 142, manufacturing parameter data150, and metrology data 160 may contain historical data (e.g., at leasta portion for training various models represented in FIG. 1 by model190). Metrology data 160 may be metrology data of produced substrates,as well as sensor data, manufacturing data, and model data correspondingto those products. Metrology data 160 may be leveraged to designprocesses for making further substrates. Predictive data 168 may includepredictions of metrology data resulting from operation of a substratesupport, predictions of component drift, aging, or failure, predictionsof component lifetimes, predictions of processing parameter evolutionover the duration of a processing operation, etc. Predictive data 168may also include data indicative of components of system 100 aging andfailing over time.

In some embodiments, predictive system 110 further includes servermachine 170 and server machine 180. Server machine 170 includes a dataset generator 172 that is capable of generating data sets (e.g., a setof data inputs and a set of target outputs) to train, validate, and/ortest model 190. Some operations of data set generator 172 are describedin detail below with respect to FIGS. 2 and 4A. In some embodiments,data set generator 172 may partition historical data (e.g., historicalsensor data, historical metrology data, etc.) into a training set (e.g.,sixty percent of the data), a validating set (e.g., twenty percent ofthe data), and a testing set (e.g., twenty percent of the data). In someembodiments, predictive system 110 (e.g., via predictive component 114)generates multiple sets of features. For example a first set of featuresmay correspond to a first set of types of sensor data (e.g., from afirst set of sensors, first combination of values from first set ofsensors, first patterns in the values from the first set of sensors)that correspond to each of the data sets (e.g., training set, validationset, and testing set) and a second set of features may correspond to asecond set of types of sensor data (e.g., from a second set of sensorsdifferent from the first set of sensors, second combination of valuesdifferent from the first combination, second patterns different from thefirst patterns) that correspond to each of the data sets. In someembodiments, training, validating, and/or testing sets may be utilizingin preparing a machine learning model for operation. In someembodiments, training, validating, and/or training sets may be utilizingin preparing a physics-based model for operation, e.g., to account forincorrect assumptions in model building, to account for unknownparameters (e.g., differences in manufacturing equipment componentswithin manufacturing tolerances), etc.

Server machine 180 includes a training engine 182, a validation engine184, selection engine 185, and/or a testing engine 186. An engine (e.g.,training engine 182, a validation engine 184, selection engine 185, anda testing engine 186) may refer to hardware (e.g., circuitry, dedicatedlogic, programmable logic, microcode, processing device, etc.), software(such as instructions run on a processing device, a general purposecomputer system, or a dedicated machine), firmware, microcode, or acombination thereof. The training engine 182 may be capable of traininga model 190 using one or more sets of features associated with thetraining set from data set generator 172. The training engine 182 maygenerate multiple trained models 190, where each trained model 190corresponds to a distinct set of features of the training set (e.g.,sensor data from a distinct set of sensors). For example, a firsttrained machine learning model may have been trained using all features(e.g., X1-X5), a second trained machine learning model may have beentrained using a first subset of the features (e.g., X1, X2, X4), and athird trained machine learning model may have been trained using asecond subset of the features (e.g., X1, X3, X4, and X5) that maypartially overlap the first subset of features. Data set generator 172may receive the output of a trained model (e.g., 190), collect that datainto training, validation, and testing data sets, and use the data setsto train a second model. Some or all of the operations of server machine180 may be used to train various types of models, includingphysics-based models, supervised machine learning models, unsupervisedmachine learning models, etc.

Validation engine 184 may be capable of validating a trained model 190using a corresponding set of features of the validation set from dataset generator 172. For example, a first trained model 190 that wastrained using a first set of features of the training set may bevalidated using the first set of features of the validation set. Thevalidation engine 184 may determine an accuracy of each of the trainedmodels 190 based on the corresponding sets of features of the validationset. The validation engine 184 may discard trained models 190 that havean accuracy that does not meet a threshold accuracy. In someembodiments, the selection engine 185 may be capable of selecting one ormore trained models 190 that have an accuracy that meets a thresholdaccuracy. In some embodiments, the selection engine 185 may be capableof selecting the trained model 190 that has the highest accuracy of thetrained models 190.

Testing engine 186 may be capable of testing a trained model 190 using acorresponding set of features of a testing set from data set generator172. For example, a first trained model 190 that was trained using afirst set of features of the training set may be tested using the firstset of features of the testing set. The testing engine 186 may determinea trained model 190 that has the highest accuracy of all of the trainedmodels based on the testing sets.

Model 190 may refer to a physics-based model describing temporalevolution of sensor data over a duration associated with a processingoperation. The physics-based model may be configured to solve equationsdescribing the flow energy, reflectance of light, interaction withacoustic stimuli, etc., in and around a substrate. The physics-basedmodel may be refined by training, e.g., measuring properties of asubstrate over a processing operation and utilizing results to refinethe physics-based model (e.g., by fitting one or more parameters to theexperimental data).

Model 190 may refer to a machine learning model, which may be the modelartifact that is created by the training engine 182 using a training setthat includes data inputs and corresponding target outputs (correctanswers for respective training inputs). Patterns in the data sets canbe found that map the data input to the target output (the correctanswer), and the machine learning model 190 is provided mappings thatcaptures these patterns. In some embodiments, machine learning model 190may predict properties of substrates. In some embodiments, machinelearning model 190 may predict failure modes of manufacturing chambercomponents. In some embodiments, machine learning model 190 may predictevolution of processing parameters over a duration associated with aprocessing operation.

Predictive component 114 may provide input data to a trained machinelearning model 190 and may run the trained machine learning model 190 onthe input to obtain one or more outputs. Predictive component 114 may becapable of determining (e.g., extracting) predictive data 168 from theoutput of the trained machine learning model 190 and may determine(e.g., extract) confidence data from the output that indicates a levelof confidence that the predictive data 168 is an accurate predictor of aprocess associated with the input data for products produced or to beproduced, or an accurate predictor of components of manufacturingequipment 124. Predictive component 114 may be capable of determiningpredictive data 168, including predictions on finished substrateproperties and predictions of effective lifetimes of components ofmanufacturing equipment 124, sensors 126, or metrology equipment 128based on the output of model 190. Predictive component 114 or correctiveaction component 122 may use the confidence data to decide whether tocause a corrective action associated with the manufacturing equipment124 based on predictive data 168.

The confidence data may include or indicate a level of confidence. As anexample, predictive data 168 may indicate the properties of a finishedwafer given a set of manufacturing inputs, including the use of asubstrate support described with substrate support data 154. Theconfidence data may indicate that the predictive data 168 is an accurateprediction for products associated with at least a portion of the inputdata. In one example, the level of confidence is a real number between 0and 1 inclusive, where 0 indicates no confidence that the predictivedata 168 is an accurate prediction for products processed according toinput data and 1 indicates absolute confidence that the predictive data168 accurately predicts properties of products processed according toinput data. Responsive to the confidence data indicating a level ofconfidence below a threshold level for a predetermined number ofinstances (e.g., percentage of instances, frequency of instances, totalnumber of instances, etc.) the predictive component 116 may cause thetrained machine learning model 190 to be re-trained (e.g., based oncurrent sensor data 146, current manufacturing parameters 150, etc.).

For purpose of illustration, rather than limitation, aspects of thedisclosure describe the training of one or more models 190 usinghistorical data and inputting current data into the one or more trainedmodels 190 to determine predictive data 168. In other implementations, aheuristic model or rule-based model is used to determine predictive data(e.g., without using a trained machine learning model). Predictivecomponent 114 may monitor historical data and metrology data 160. Any ofthe information described with respect to data inputs 210 of FIG. 2 maybe monitored or otherwise used in the heuristic or rule-based model.

In some embodiments, the functions of client device 120, predictiveserver 112, server machine 170, and server machine 180 may be providedby a fewer number of machines. For example, in some embodiments servermachines 170 and 180 may be integrated into a single machine, while insome other embodiments, server machine 170, server machine 180, andpredictive server 112 may be integrated into a single machine. In someembodiments, client device 120 and predictive server 112 may beintegrated into a single machine.

In general, functions described in one embodiment as being performed byclient device 120, predictive server 112, server machine 170, and servermachine 180 can also be performed on predictive server 112 in otherembodiments, if appropriate. In addition, the functionality attributedto a particular component can be performed by different or multiplecomponents operating together. For example, in some embodiments,predictive server 112 may determine the corrective action based on thepredictive data 168. In another example, client device 120 may determinethe predictive data 168 based on output from the trained machinelearning model or the physics-based model.

In addition, the functions of a particular component can be performed bydifferent or multiple components operating together. One or more ofpredictive server 112, server machine 170, or server machine 180 may beaccessed as a service provided to other systems or devices throughappropriate application programming interfaces (API).

In embodiments, a “user” may be represented as a single individual.However, other embodiments of the disclosure encompass a “user” being anentity controlled by a plurality of users and/or an automated source.For example, a set of individual users federated as a group ofadministrators may be considered a “user.”

Embodiments of the disclosure may be applied to data quality evaluation,feature enhancement, model evaluation, Virtual Metrology (VM),Predictive Maintenance (PdM), limit optimization, or the like.

Although embodiments of the disclosure are discussed in terms ofgenerating predictive data 168 to perform a corrective action inmanufacturing facilities (e.g., semiconductor manufacturing facilities),embodiments may also be generally applied to improved data processing byutilizing multidimensional sensor data to perform a holistic data fit,and use the fitted data to improve processing conditions, parameters,set points, processes, etc.

FIG. 2 is a block diagram of an example data set generator 272 (e.g.,data set generator 172 of FIG. 1 ), used to create data sets for a model(e.g., model 190 of FIG. 1 ), according to some embodiments. A data setgenerator 272 may be part of server machine 170 of FIG. 1 . In someembodiments, system 100 of FIG. 1 includes multiple models. In suchcases, each model may have a separate data set generator, or models mayshare a data set generator. Depicted in FIG. 2 is a data set generatorassociated with a machine learning model configured to take as inputmultidimensional sensor data associated with processing of a substrate(e.g., in-situ optical substrate reflectance data). The machine learningmodel is configured to provide as output information indicative ofevolution of one or more processing parameters over a durationassociated with performing a processing operation. Similar data setgenerators may be utilized for machine learning models performing otherfunctions, substituting types of input and output data to align withtarget functionality. In some embodiments, a machine learning modelincluded in model 190 may be an unsupervised or a semi-supervised model,e.g., is to be trained using at least a portion of unlabeled trainingdata. A data set generator similar to data set generator 272 may be usedto generate data sets for training an unsupervised or semi-supervisedmodel, e.g., by generating sets of training input data withoutgenerating associated target output data. In some embodiments, aphysics-based model is to be trained (e.g., physics-based model is to beadjusted or refined based on measured data). A data set generatorsimilar to data set generator 272 may be utilized to generate data setsfor a physics-based model.

Referring to FIG. 2 , system 200 containing data set generator 272(e.g., data set generator 172 of FIG. 1 ) creates data sets for amachine learning model (e.g., model 190 of FIG. 1 ). Data set generator272 may create data sets using data retrieved as output from sensorsassociated with a processing chamber, metrology measurements associatedwith a substrate, etc. In some embodiments, data set generator 272creates training input, validating input, testing input, etc. frommultidimensional sensor data associated with one or more processingoperations. Data set generator 272 also generates target output 220 fortraining a machine learning model. Target output includes dataindicative of a temporal evolution of one or more processing parametersthrough a duration associated with the processing operation, e.g., etchdepth, etch rate, deposition rate, structure dimensions, etc. In someembodiments, a machine learning model may be utilized to perform adifferent task, with corresponding changes to input and target outputdata. Data input 210 and target output 220 are supplied to a machinelearning model for training, testing, validating, etc.

It is within the scope of this disclosure for training input 210 andtarget output 220 to be represented in a variety of different ways. Avector or matrix of values, lists, images, and other data types may allbe used as data input 210 and target output 220.

In some embodiments, data set generator 272 generates a data set (e.g.,training set, validating set, testing set) that includes one or moredata inputs 210 (e.g., training input, validating input, testing input)and may include one or more target outputs 220 that correspond to thedata inputs 210. The data set may also include mapping data that mapsthe data inputs 210 to the target outputs 220. Data inputs 210 may alsobe referred to as “features,” “attributes,” or “information.” In someembodiments, data set generator 272 may provide the data set to thetraining engine 182, validating engine 184, or testing engine 186 ofFIG. 1 , where the data set is used to train, validate, or test machinelearning model 190 of FIG. 1 . Some embodiments of generating a trainingset may further be described with respect to FIG. 4A.

In some embodiments, data set generator 272 may generate a first datainput corresponding to a first set of multidimensional sensor data 242Ato train, validate, or test a first machine learning model. Data setgenerator 272 may generate a second data input corresponding to a secondset of multidimensional sensor data 242B to train, validate, or test asecond machine learning model.

In some embodiments, data set generator 272 may perform operations onone or more of data input 210 and target output 220. Data set generator272 may extract patterns from the data (slope, curvature, etc.), maycombine data (average, feature production, etc.), or may separate datainto groups (e.g., train a model on a subset of the multidimensionalsensor data) and use the groups to train separate models.

Data inputs 210 and target outputs 220 to train, validate, or test amachine learning model may include information for a particularprocessing chamber. Data inputs 210 and target outputs 220 may includeinformation for a particular product design (e.g., used for allsubstrates of that design). Data inputs 210 and target outputs 220 mayinclude information for a particular type of processing, targetsubstrate property, target processing chamber fleet, or may be groupedtogether in another way.

In some embodiments, data set generator 272 may generate a set of targetoutput 220, including evolution of processing parameter 230. Targetoutput 220 may be separated into sets corresponding to sets of inputdata. Different sets of target output 220 may be used in connection withthe similarly defined sets of data input 210, including trainingdifferent models, using different sets for training, validating, andtesting, etc.

Target output 220 may be generated by measuring evolution of one or moretarget processing parameters (e.g., using a method other than machinelearning). In some embodiments, a machine learning model may beconfigured to output one or more corrective actions, e.g., recommendedrecipe updates. Corrective action output 220 may be generated bycorrelating trends in performance data to appropriate corrective actions(e.g., by using a method other than machine learning). A user mayindicate that performing a particular corrective action addressed adifference between historical predicted performance and measuredperformance, a manufacturing fault may be intentionally introduced togenerate data useful for training, etc. In some embodiments, a model maybe trained without target output 220 (e.g., an unsupervised orsemi-supervised model). A model trained that is not provided with targetoutput may, for example, be trained to recognize significant (e.g.,outside an error threshold) differences between predicted and measuredperformance data.

In some embodiments, the information used to train the machine learningmodel may be from specific types of manufacturing equipment (e.g.,manufacturing equipment 124 of FIG. 1 ) of the manufacturing facilityhaving specific characteristics and allow the trained machine learningmodel to determine outcomes for a specific group of manufacturingequipment 124 based on input of multidimensional sensor data associatedwith one or more components sharing characteristics of the specificgroup. In some embodiments, the information used to train the machinelearning model may be for components from two or more manufacturingfacilities and may allow the trained machine learning model to determineoutcomes for components based on input from one manufacturing facility.

In some embodiments, subsequent to generating a data set and training,validating, or testing a machine learning model using the data set, themachine learning model may be further trained, validated, or tested, oradjusted, e.g., via retraining procedures.

In some embodiments, a data set generator similar to data set generator272 may be utilized for training (e.g., updating, refining, etc.) aphysics-based model. A physics-based model may be trained, for example,to correct for inaccurate assumptions (e.g., approximations inequations, material properties, etc.), correct for unknown information(e.g., differences within manufacturing tolerance in manufacturedcomponents), etc. Training a physics-based model may include adjustingparameters of the model to decrease residuals between model output andtarget output, e.g., by gradient descent.

FIG. 3 is a block diagram illustrating system 300 for generating outputdata (e.g., predictive data 168 of FIG. 1 ), according to someembodiments. System 300 may be used to analyze multidimensional sensordata associated with one or more processing operations, and output anindication of temporal evolution of processing parameters through aduration of the processing operation. A system similar to system 300 maybe used for other models, such as a machine learning model that receivesmultidimensional sensor data and generates indications of predictedanomalies, suggested corrective actions, etc. Some or all of theoperations of system 300 may be used to generate data indicative ofevolution of one or more substrate and/or processing parameters via aphysics-based model. In these cases, other data may be used as input andproduced as output by system 300, as appropriate.

Referring to FIG. 3 , at block 310, the system 300 (e.g., components ofpredictive system 110 of FIG. 1 ) performs data partitioning (e.g., viadata set generator 172 of server machine 170 of FIG. 1 ) of historicaldata 364 (e.g., historical multidimensional sensor data, historicalsubstrate property evolution data, etc.) to generate training set 302,validation set 304, and testing set 306. For example, the training setmay be 60% of the data, the validation set may be 20% of the data, andthe testing set may be 20% of the data.

At block 312, system 300 performs model training (e.g., via trainingengine 182 of FIG. 1 ) using the training set 302. System 300 may trainone model or may train multiple models using multiple sets of featuresof the training set 302 (e.g., a first set of features including asubset of multidimensional data of the training set 302, a second set offeatures including a different subset of multidimensional sensor data ofthe training set 302, etc.). For example, system 300 may train a machinelearning model to generate a first trained machine learning model usingthe first set of features in the training set and to generate a secondtrained machine learning model using the second set of features in thetraining set (e.g., different data than the data used to train the firstmachine learning model). In some embodiments, the first trained machinelearning model and the second trained machine learning model may becombined to generate a third trained machine learning model (e.g., whichmay be a better predictor than the first or the second trained machinelearning model on its own). In some embodiments, sets of features usedin comparing models may overlap (e.g., one model may be trained withmultidimensional sensor data associated with a first set of wavelengthsof optical reflectance data, and another model with multidimensionalsensor data indicative of a second set of wavelengths of opticalreflectance data where the second set includes one or more wavelengthsin the first set, different models may be trained with data fromdifferent locations of a substrate, etc.). In some embodiments, hundredsof models may be generated including models with various permutations offeatures and combinations of models.

At block 314, the system 300 performs model validation (e.g., viavalidation engine 184 of FIG. 1 ) using the validation set 304. System300 may validate each of the trained models using a corresponding set offeatures of the validation set 304. For instance, validation set 304 mayuse the same subset of performance data used in training set 302, butfor different input conditions. In some embodiments, the system 300A mayvalidate hundreds of models (e.g., models with various permutations offeatures, combinations of models, etc.) generated at block 312. At block314, the system 300 may determine an accuracy of each of the one or moretrained models (e.g., via model validation) and may determine whetherone or more of the trained models has an accuracy that meets a thresholdaccuracy. Responsive to determining that none of the trained models hasan accuracy that meets a threshold accuracy, flow returns to block 312where the system 300 performs model training using different sets offeatures of the training set. Responsive to determining that one or moreof the trained models has an accuracy that meets a threshold accuracy,flow continues to block 316. The system 300 may discard the trainedmachine learning models that have an accuracy that is below thethreshold accuracy (e.g., based on the validation set).

At block 316, system 300 may perform model selection (e.g., viaselection engine 185 of FIG. 1 ) to determine which of the one or moretrained models that meet the threshold accuracy has the highest accuracy(e.g., the selected model 308, based on the validating of block 314).Operations of block 316 may be skipped, e.g., if only one model wastrained. Responsive to determining that two or more of the trainedmodels that meet the threshold accuracy have the same accuracy, flow mayreturn to block 312 where the system 300 performs model training usingfurther refined training sets corresponding to further refined sets offeatures for determining a trained model that has the highest accuracy.

At block 318 system 300 performs model testing (e.g., via testing engine186 of FIG. 1 ) using the testing set 306 to test the selected model308. The system 300 may test, using the first set of features in thetesting set, the first trained machine learning model to determine thefirst trained machine learning model meets a threshold accuracy (e.g.,based on the first set of features of the testing set 306). Responsiveto accuracy of the selected model 308 not meeting the threshold accuracy(e.g., the selected model 308 is overly fit to the training set 302and/or validation set 304 and is not applicable to other data sets suchas the testing set 306), flow continues to block 312 where the system30A performs model training (e.g., retraining) using different trainingsets possibly corresponding to different sets of features or areorganization of substrates split into training, validation, andtesting sets. Responsive to determining that the selected model 308 hasan accuracy that meets a threshold accuracy based on the testing set306, flow continues to block 320. In at least block 312, the model maylearn patterns in the multidimensional sensor data to make predictionsand in block 318, the system 300 may apply the model on the remainingdata (e.g., testing set 306) to test the predictions.

At block 320, system 300 uses the trained model (e.g., selected model308) to receive current data 354 (e.g., current multidimensional sensordata associated with a substrate not included in historical data 364)and determines (e.g., extracts), from the output of the trained model,predictive data 368 (e.g., predictive data 168 of FIG. 1 ). In someembodiments, predictive data 368 is indicative of an action, e.g., mayinclude a recommendation to perform a corrective action (e.g., perform acorrective action in association with manufacturing equipment 124 ofFIG. 1 , provide and alert to client device 120 of FIG. 1 , etc.).

In some embodiments, retraining of the machine learning model occurs bysupplying additional data to further train the model. Current data 354may be provided at block 312. Additional temporal evolution data 346(e.g., data indicative of evolution through a duration of a processingoperation of properties such as etch depth, etch rate, deposition rate,etc.) may be provided as well. These data may be different from the dataoriginally used to train the model by incorporating combinations ofinput parameters not part of the original training, input parametersoutside the parameter space spanned by the original training, or may beupdated to reflect chamber specific knowledge (e.g., differences from anideal chamber due to manufacturing tolerance ranges, aging components,etc.). Selected model 308 may be retrained based on this data.

In some embodiments, one or more of the acts 310-320 may occur invarious orders and/or with other acts not presented and describedherein. In some embodiments, one or more of acts 310-320 may not beperformed. For example, in some embodiments, one or more of datapartitioning of block 310, model validation of block 314, modelselection of block 316, or model testing of block 318 may not beperformed. In training a physics-based model, e.g., to take as inputmultidimensional sensor data and produce as output predicted temporalevolution of one or more processing parameters, a subset of theseoperations may be performed.

FIGS. 4A-C are flow diagrams of methods 400A-C associated with analysisof multidimensional sensor data, according to some embodiments. Methods400A-C may be performed by processing logic that may include hardware(e.g., circuitry, dedicated logic, programmable logic, microcode,processing device, etc.), software (such as instructions run on aprocessing device, a general purpose computer system, or a dedicatedmachine), firmware, microcode, or a combination thereof. In someembodiment, methods 400A-C may be performed, in part, by predictivesystem 110. Method 400A may be performed, in part, by predictive system110 (e.g., server machine 170 and data set generator 172 of FIG. 1 ,data set generator 272 of FIG. 2 ). Predictive system 110 may use method400A to generate a data set to at least one of train, validate, or testa model, in accordance with embodiments of the disclosure. The model maybe a physics-based digital twin model (e.g., to generate predictiveperformance data of a substrate support), a machine learning model(e.g., to generate predictive performance data of a wafer, to generatedata indicative of a corrective action associated with a component ofmanufacturing equipment, etc.), a statistical model, or another modeltrained to receive input and generate output related to substratesupport characterization. Method 400B may be performed by predictiveserver 112 (e.g., predictive component 114, etc.). Method 400C may beperformed by server machine 180 (e.g., training engine 182). In someembodiments, a non-transitory storage medium stores instructions thatwhen executed by a processing device (e.g., of predictive system 110, ofserver machine 180, of predictive server 112, etc.) cause the processingdevice to perform one or more of methods 400A-C.

For simplicity of explanation, methods 400A-C are depicted and describedas a series of operations. However, operations in accordance with thisdisclosure can occur in various orders and/or concurrently and withother operations not presented and described herein. Furthermore, notall illustrated operations may be performed to implement methods 400A-Cin accordance with the disclosed subject matter. In addition, thoseskilled in the art will understand and appreciate that methods 400A-Ccould alternatively be represented as a series of interrelated statesvia a state diagram or events.

FIG. 4A is a flow diagram of a method 400A for generating a data set fora machine learning model for generating predictive data (e.g.,predictive data 168 of FIG. 1 ), according to some embodiments.

Referring to FIG. 4A, in some embodiments, at block 401 processing logicimplementing method 400A initializes a training set T to an empty set.At block 402, processing logic generates first data input (e.g., firsttraining input, first validating input) that may includemultidimensional sensor data, processing chamber performance data,measured substrate performance data, substrate metrology data (e.g.,film properties such as thickness, material composition, opticalproperties, roughness, and so on), etc. In some embodiments, the firstdata input may include a first set of features for first types of dataand a second data input may include a second set of features for secondtypes of data (e.g., as described with respect to FIG. 3 ).

At block 403, processing logic generates a first target output for oneor more of the data inputs (e.g., first data input). In someembodiments, the first target output includes an indication of temporalevolution of one or more processing parameters through a duration of aprocessing operation, e.g., etch depth, etch rate, substrate structuredimension, etc. In some embodiments, the first target output isperformance data of substrates. In some embodiments, the first targetoutput includes data indicative of a corrective action. In someembodiments, no target output is generated (e.g., for training anunsupervised machine learning model)

At block 404, processing logic optionally generates mapping data that isindicative of an input/output mapping. The input/output mapping (ormapping data) may refer to the data input (e.g., one or more of the datainputs described herein), the target output for the data input, and anassociation between the data input(s) and the target output. In someembodiments (e.g., those without target output data) these operationsmay not be performed.

At block 405, processing logic adds the mapping data generated at block404 to data set T, in some embodiments. At block 406, processing logicbranches based on whether data set T is sufficient for at least one oftraining, validating, and/or testing model 190 of FIG. 1 . If so,execution proceeds to block 407, otherwise, execution continues back atblock 402. It should be noted that in some embodiments, the sufficiencyof data set T may be determined based simply on the number of inputs,mapped in some embodiments to outputs, in the data set, while in someother implementations, the sufficiency of data set T may be determinedbased on one or more other criteria (e.g., a measure of diversity of thedata examples, accuracy, etc.) in addition to, or instead of, the numberof inputs.

At block 407, processing logic provides data set T (e.g., to servermachine 180 of FIG. 1 ) to train, validate, and/or test model 190. Insome embodiments, data set T is a training set and is provided totraining engine 182 of server machine 180 to perform the training. Insome embodiments, data set T is a validation set and is provided tovalidation engine 184 of server machine 180 to perform the validating.In some embodiments, data set T is a testing set and is provided totesting engine 186 of server machine 180 to perform the testing.

FIG. 4B is a flow diagram of a method 400B for performing temporalanalysis of multidimensional sensor data, according to some embodiments.At block 410 of method 400B, processing logic receives first data. Thefirst data includes data from one or more sensors of a processingchamber. The first data is associated with a processing operation. Thefirst data is multidimensional, e.g., is resolved in at least twodimensions. The first data is resolved in time, e.g., one of thedimensions of the multidimensional data is time.

In some embodiments, the first data includes repeated measurements byone or more sensors through a duration (e.g., the duration of aprocessing operation). In some embodiments, sensor measurements may berepeated multiple times throughout a processing operation, e.g., may berepeated in multiple time steps, time frames, etc. In some embodiments,different data is collected at different frames, e.g., not every pointrepresented in dimensions independent from the time dimensions may berepresented at every data collection time. For example, multidimensionalsensor data may include spectral data. In some embodiments, a firstsubset of wavelength measurements may be recorded at a first time step,a second subset of wavelength measurements may be recorded at a secondtime step, etc.

In some embodiments, first data includes in-situ measurements of asubstrate in a processing chamber. As used herein, in-situ measurementindicates measurements taken during processing of a substrate, e.g.,optical reflectance data recording during a processing operation.In-situ measurements may include frequency dependent measurements, e.g.,spectrally resolved measurements (optical reflectance, x-raymeasurements, etc.), acoustic measurements, etc.

At block 412, processing logic provides the first data to a model (e.g.,model 190 of FIG. 1 ). In some embodiments, the model includes aphysics-based model. In some embodiments, the model includes a machinelearning model. At block 414, processing logic receives from the modelsecond data. The second data includes an indication of an evolution ofone or more processing parameters (e.g., etch rate, etch depth,structure geometry, deposition rate, etc.) during a processing operation(e.g., throughout the duration of a processing operation).

The model may be configured to receive as input multidimensional sensordata (e.g., data resolved in time and at least one other dimension) andfit the data simultaneously to generate an indication of evolution of aprocess operation, evolution of one or more dimensions of a substrate,etc. The model may be configured to treat the input data holistically,e.g., treat data from throughout the duration of a processing operationsimultaneously. In some embodiments, the model may be configured togenerate as output data indicative of a corrective action, e.g.,adjustment to a recipe, recommended maintenance, recommended componentreplacement, etc. Operations of the model are discussed further inconnection with FIG. 5 . At block 416, processing logic causesperformance of a corrective action in view of the second data. Causingperformance of a collective action may include updating a processrecipe, scheduling maintenance (e.g., scheduling preventativemaintenance, scheduling corrective maintenance, etc.), sending an alertto a user, etc.

FIG. 4C is a flow diagram of a method 400C for utilizing a machinelearning model in connection with analysis of multidimensional sensordata, according to some embodiments. At block 420, processing logicreceives first historical data. The first historical data includes datafrom one or more sensors of one or more processing chambers. The firsthistorical data is associated with one or more processing operations.The first historical data is multidimensional, e.g., is resolved in atleast time and one other dimension. First historical data may includesensor data associated with many substrates, many processing runs, etc.

At block 422, processing logic receives second historical data. Thesecond historical data includes an indication of an evolution of aprocessing parameter during a processing operation. For example, thesecond historical data may include data indicating a temporal evolutionof etch depth, etch rate, deposition rate, geometry of a structure,etc., through the duration of a processing operation. Second data may becorrelated to the first data, e.g., data indicating a link betweensensor data and temporal processing parameter evolution data associatedwith the same substrate, same chamber, same substrate design, sameprocess recipe, or the like may be present.

At block 424, processing logic trains a machine learning model. Trainingthe machine learning model includes providing training input data to themachine learning model. Training the machine learning model may includeproviding target output data to the machine learning model. The machinelearning model may be configured to receive data similar (e.g., of thesame type, having the same source, etc.) to the training input data andgenerate data similar to the target output data. The machine learningmodel of method 400C may include one or more of a neural network (e.g.,artificial neural network), Support Vector machine, Radial BasisFunction, clustering, k-Nearest Neighbor algorithm, random forest, etc.Training a machine learning model is described in more detail inconnection with FIG. 3 .

At block 426, processing logic receives first current data. The firstcurrent data incudes data of the same type as the first historical datareceived at block 420 (e.g., multidimensional sensor data). Firstcurrent data may have features in common with first data received atblock 410 of FIG. 4B. At block 428, the first current data is providedto the trained machine learning model.

At block 430, processing logic receives second current data from thetrained machine learning model. The second current data includes data ofthe same type as the second historical data received at block 422 (e.g.,data indicative of temporal evolution of one or more processingparameters). Second current data may have features in common with seconddata received at block 414 of FIG. 4B. At block 432, processing logiccauses performance of a corrective action in view of the second currentdata. Operations of block 432 may have features in common withoperations of block 416 of FIG. 4B.

FIG. 5 depicts a data analysis system 500 for utilizing multidimensionalsensor data 502 to generate predictive data 520, according to someembodiments. In some embodiments, data indicative of performance of aprocessing operation is generated by processing chamber sensors 526(e.g., sensors 126 of FIG. 1 ). Processing chamber sensors 526 mayinclude sensors measuring chamber temperature, chamber pressure,supplied electrical power, electrical resistance, optical properties,gas flow rate, chemical properties, acoustic properties, etc. In someembodiments, processing chamber sensors 526 include sensors receivingoptical data, e.g., substrate reflectance spectra. Processing chambersensors 526 generate multidimensional sensor data 502. Multidimensionalsensor data 502 may be resolved in at least time and one otherdimension. For example, data generated by an optical sensor may beresolved in frequency/wavelength, and time. As another example, pressureand temperature data may be resolved in time and sensor (e.g., sensorlocation, sensor ID, etc.). In some embodiments, all resolved datapoints in one dimension may be measured repeatedly in time. For example,spectral data may resolve signal at a number of wavelengths, andmeasurement of those wavelengths may be repeated several timesthroughout the duration of a processing operation. In some embodiments,a selection of data points may be measured at one or more time steps.For example, an acoustic sensor may cycle measured frequencies in time,such that a data point is not measured for each measureable frequency ateach time step. In some embodiments, multidimensional sensor data 502may include data from the entire duration of a processing operation,e.g., may be provided to modeling system 510 after the processingoperation has concluded. In some embodiments, multidimensional sensordata 502 may include data from a portion of the duration of theprocessing operation.

Multidimensional sensor data 502 is provided to modeling system 510.Modeling system 510 may be included in predictive system 110 of FIG. 1 ,may be hosted fully or in part on predictive server 112 of FIG. 1 , etc.Modeling system 510 may be configured to fit multidimensional sensordata 502 in at least two dimensions simultaneously, e.g., modelingsystem 510 may include components, algorithms, etc., configured to fitthe time evolution of multiple data collection channels (e.g., sensors,wavelengths, frequencies, locations, etc.). Fitting multiple dimensionsof multidimensional data simultaneously provides technical advantagesover conventional methods. An increase in the number of data pointsavailable for fitting (e.g., due to fitting data from multiple timesteps or time frames together) may increase the number of parametersthat may be fit, may increase the number of physical dimensions that maybe predicted, may increase certainty of process evolution (e.g., changein etch rate over the duration of a processing operation), may increaserobustness of fitting procedures against noisy signals, etc. Increasedavailable information, increased certainly, and increased ability toextract information from noisy signals may increase efficiency of dataprocessing, e.g., fewer test operations may need to be run to indicateperformance of a new recipe, new chamber, new operation, new substratedesign, or the like. Reliable analytics of temporal evolution ofprocessing parameters increases efficiency of process learning, e.g.,development of new process recipes, development of new product designs,etc.

Modeling system 510 may include data fitting module 512, physical model514, and data simulator 516. Multidimensional data may be provided todata fitting module 512. Data fitting module 512 may be configured toreceive as input multidimensional sensor data and generate as output adescription of the data, e.g., a fit, a functional form, etc. Datafitting module 512 may generate output using a regression model, arule-based model, etc. Data fitting module 512 may fit the values of oneor more parameters in view of the input data. Data fitting module 512may fit multiple values over time, e.g., response over time frommultiple sensors, response over time of multiple properties measured bya sensor (e.g., optical wavelength, acoustic frequency, etc.), or thelike.

Output of data fitting module 512 may be provided to physical model 514.Physical model 514 may be configured to accept data (e.g., fittedparameter values from data fitting module 512) and generate anindication of properties of a manufactured device. For example, physicalmodel 514 may accept fitted parameters from fitting module 512 andgenerate an indication of evolution of substrate metrology over theduration of a processing operation. Physical model 514 may express theevolution of multiple properties over time. For example, physical model514 may express evolution of etch depth, etch width, deposition depth,structure dimension, or the like, over a time duration (e.g., theduration of the processing operation associated with multidimensionalsensor data 502).

Data indicative of predicted physical system evolution (e.g., output ofphysical model 514) may be provided to data simulator 516. Datasimulator 516 may be configured to generate synthetic sensor data inaccordance with a physical model. Data simulator 516 may incorporate amodel of a physical object (e.g., substrate) interacting with a medium(e.g., electromagnetic radiation). Data simulator 516 may include adigital twin model. As used herein, a digital twin is a digital replicaof a physical asset, such as a manufactured part or substrate. Thedigital twin includes characteristics of the physical asset, such ascoordinate axes dimensions, weight characteristics, materialcharacteristics (e.g., density, surface roughness), opticalcharacteristics (e.g., reflectivity), etc. In some embodiments, datasimulator 516 may solve equations with physical meaning (e.g., systemsof equations describing the interaction of structures and radiation) togenerate synthetic sensor data. In some embodiments, output of datasimulator 516 may be provided to fitting module 512. Predictions may beimproved recursively until sufficient accuracy is reached (e.g., untilsolutions converge within a threshold value). Modeling system 510 maythen output predictive data 520. Predictive data 520 may share featureswith predictive data 168 of FIG. 1 . Predictive data may include adescription of predicted evolution of substrate parameters over theduration of the processing operation. For example, predictive data 520may include an indication of etch depth evolution over the duration of aprocessing operation.

In some embodiments, functions of system 500 may be performed by one ormore machine learning models. In some embodiments, functions of modelingsystem 510 may be performed by one or more machine learning models. Insome embodiments, modeling system 510 may be replaced with a machinelearning model. The machine learning model may be trained in asupervised manner (e.g., trained using labeled training data). Themachine learning model may be trained in a semi-supervised manner (e.g.,trained using some labeled training data and some unlabeled trainingdata). The machine learning model may be provided with sensor data astraining input and provided with labels indicative of substratemetrology (e.g., etch depth, deposition depth, etc.) as target output.In operation, The trained machine learning model may be provided withmultidimensional sensor data 502 and generate as output predictive data520.

In some embodiments, physical model 514 may be replaced with a machinelearning model. In training, the machine learning model may be providedwith fit data (e.g., fit parameters generated from multidimensionalsensor data 502) as training input and provided with indications of oneor more substrate dimensions (e.g., etch depth) as target output. Inoperation, the machine learning model may receive fit output fromfitting module 512 and generate as output a prediction of physicaldimensions of a substrate. Other operations of system 500 may instead bereplaced with machine learning models, with corresponding changes totraining input and target output provided in training operations andinput provided and output generated in model operation.

FIG. 6 is a block diagram illustrating a computer system 600, accordingto some embodiments. In some embodiments, computer system 600 may beconnected (e.g., via a network, such as a Local Area Network (LAN), anintranet, an extranet, or the Internet) to other computer systems.Computer system 600 may operate in the capacity of a server or a clientcomputer in a client-server environment, or as a peer computer in apeer-to-peer or distributed network environment. Computer system 600 maybe provided by a personal computer (PC), a tablet PC, a Set-Top Box(STB), a Personal Digital Assistant (PDA), a cellular telephone, a webappliance, a server, a network router, switch or bridge, cloud-basedcomputational device, virtual computing devices, or any device capableof executing a set of instructions (sequential or otherwise) thatspecify actions to be taken by that device. Further, the term “computer”shall include any collection of computers that individually or jointlyexecute a set (or multiple sets) of instructions to perform any one ormore of the methods described herein.

In a further aspect, computer system 600 may include a processing device602, a volatile memory 604 (e.g., Random Access Memory (RAM)), anon-volatile memory 606 (e.g., Read-Only Memory (ROM) orElectrically-Erasable Programmable ROM (EEPROM)), and a data storagedevice 618, which may communicate with each other via a bus 608.

Processing device 602 may be provided by one or more processors such asa general purpose processor (such as, for example, a Complex InstructionSet Computing (CISC) microprocessor, a Reduced Instruction Set Computing(RISC) microprocessor, a Very Long Instruction Word (VLIW)microprocessor, a microprocessor implementing other types of instructionsets, or a microprocessor implementing a combination of types ofinstruction sets) or a specialized processor (such as, for example, anApplication Specific Integrated Circuit (ASIC), a Field ProgrammableGate Array (FPGA), a Digital Signal Processor (DSP), or a networkprocessor).

Computer system 600 may further include a network interface device 622(e.g., coupled to network 674). Computer system 600 also may include avideo display unit 610 (e.g., an LCD), an alphanumeric input device 612(e.g., a keyboard), a cursor control device 614 (e.g., a mouse), and asignal generation device 620.

In some implementations, data storage device 618 may include anon-transitory computer-readable storage medium 624 (e.g.,non-transitory machine-readable storage medium) on which may storeinstructions 626 encoding any one or more of the methods or functionsdescribed herein, including instructions encoding components of FIG. 1(e.g., predictive component 114, model(s) 190, etc.) and forimplementing methods described herein.

Instructions 626 may also reside, completely or partially, withinvolatile memory 604 and/or within processing device 602 during executionthereof by computer system 600, hence, volatile memory 604 andprocessing device 602 may also constitute machine-readable storagemedia.

While computer-readable storage medium 624 is shown in the illustrativeexamples as a single medium, the term “computer-readable storage medium”shall include a single medium or multiple media (e.g., a centralized ordistributed database, and/or associated caches and servers) that storethe one or more sets of executable instructions. The term“computer-readable storage medium” shall also include any tangiblemedium that is capable of storing or encoding a set of instructions forexecution by a computer that cause the computer to perform any one ormore of the methods described herein. The term “computer-readablestorage medium” shall include, but not be limited to, solid-statememories, optical media, and magnetic media.

The methods, components, and features described herein may beimplemented by discrete hardware components or may be integrated in thefunctionality of other hardware components such as ASICS, FPGAs, DSPs orsimilar devices. In addition, the methods, components, and features maybe implemented by firmware modules or functional circuitry withinhardware devices. Further, the methods, components, and features may beimplemented in any combination of hardware devices and computer programcomponents, or in computer programs.

Unless specifically stated otherwise, terms such as “receiving,”“performing,” “providing,” “obtaining,” “causing,” “accessing,”“determining,” “adding,” “using,” “training,” “generating,”“identifying,” “assigning,” “updating,” “scheduling,” “correcting,” orthe like, refer to actions and processes performed or implemented bycomputer systems that manipulate and transform data represented asphysical (electronic) quantities within the computer system registersand memories into other data similarly represented as physicalquantities within the computer system memories or registers or othersuch information storage, transmission or display devices. Also, theterms “first,” “second,” “third,” “fourth,” etc. as used herein aremeant as labels to distinguish among different elements and may not havean ordinal meaning according to their numerical designation.

Examples described herein also relate to an apparatus for performing themethods described herein. This apparatus may be specially constructedfor performing the methods described herein, or it may include a generalpurpose computer system selectively programmed by a computer programstored in the computer system. Such a computer program may be stored ina computer-readable tangible storage medium.

The methods and illustrative examples described herein are notinherently related to any particular computer or other apparatus.Various general purpose systems may be used in accordance with theteachings described herein, or it may prove convenient to construct morespecialized apparatus to perform methods described herein and/or each oftheir individual functions, routines, subroutines, or operations.Examples of the structure for a variety of these systems are set forthin the description above.

The above description is intended to be illustrative, and notrestrictive. Although the present disclosure has been described withreferences to specific illustrative examples and implementations, itwill be recognized that the present disclosure is not limited to theexamples and implementations described. The scope of the disclosureshould be determined with reference to the following claims, along withthe full scope of equivalents to which the claims are entitled.

1. A method, comprising: receiving, by a processing device, first data,wherein the first data comprises data generated by one or more sensorsof a processing chamber associated with a processing operation, andwherein the first data is resolved in at least two dimensions, whereinone of the at least two dimensions is time; providing the first data asinput to a model, wherein the model is configured to fit a temporalevolution of the first data over a duration associated with theprocessing operation; obtaining second data as output of the model,wherein the second data comprises an indication of an evolution of aprocessing parameter during the processing operation; and causingperformance of a corrective action in view of the second data.
 2. Themethod of claim 1, wherein the first data comprises in-situ measurementsof a substrate in the processing chamber.
 3. The method of claim 1,wherein the at least two dimensions comprise frequency of a signal. 4.The method of claim 1, wherein the first data comprises spectrallyresolved data associated with detecting electromagnetic radiation. 5.The method of claim 1, wherein the model comprises a physics-basedmodel, and wherein the model is configured to fit parameters to amulti-dimensional fit function.
 6. The method of claim 1, wherein themodel comprises a trained machine learning model.
 7. The method of claim6, further comprising: receiving first historical data, wherein thefirst historical data is of the same type as the first data; receivingsecond historical data, wherein the second historical data is of thesame type as the second data; and training the machine learning model byproviding the first historical data as training input and the secondhistorical data as target output.
 8. The method of claim 1, whereincausing performance of the corrective action in view of the second datacomprises: providing a user interface that presents the indication ofthe evolution of the processing parameter during the processingoperation; receiving, via the user interface, user input; anddetermining the corrective action based on the user input, wherein thecorrective action comprises one or more of: updating a process recipe;scheduling corrective maintenance; scheduling preventative maintenance;or sending an alert to a user.
 9. The method of claim 1, wherein theprocess parameter comprises an etch rate or a deposition rate.
 10. Asystem, comprising memory and a processing device coupled to the memory,wherein the processing device is to: receive first data, wherein thefirst data comprises data from one or more sensors of a processingchamber associated with a processing operation, and wherein the firstdata is resolved in at least two dimensions, wherein one of the at leasttwo dimensions is time; provide the first data to a model; receive, fromthe model, second data, wherein the second data comprises an indicationof an evolution of a processing parameter during the processingoperation; and cause performance of a corrective action in view of thesecond data.
 11. The system of claim 10, wherein the first datacomprises in-situ measurements of a substrate in the processing chamber.12. The system of claim 10, wherein one of the at least two dimensionscomprises frequency.
 13. The system of claim 10, wherein the first datacomprises data associated with spectrally resolved detection ofelectromagnetic radiation.
 14. The system of claim 10, wherein the modelcomprises a physics-based model, and wherein the model fits a temporalevolution of the first data over a duration associated with theprocessing operation.
 15. The system of claim 14, wherein the processingdevice is further to: receive first historical data, wherein the firsthistorical data is of the same type as the first data; receive secondhistorical data, wherein the second historical data is of the same typeas the second data; and training a machine learning model by providingthe first historical data as training input and the second historicaldata as target output.
 16. The system of claim 10, wherein the processparameter comprises an etch rate or a deposition rate.
 17. Anon-transitory machine-readable storage medium storing instructionswhich, when executed, cause a processing device to perform operationscomprising: receiving first data, wherein the first data comprises datagenerated by one or more sensors of a processing chamber associated witha processing operation, and wherein the first data is resolved in atleast two dimensions, wherein one of the at least two dimensions istime; providing the first data to a model; receiving, from the model,second data, wherein the second data comprises an indication of anevolution of a processing parameter during the processing operation; andcausing performance of a corrective action in view of the second data.18. The non-transitory machine-readable storage medium of claim 17,wherein the first data comprises in-situ measurements of a substrate inthe processing chamber.
 19. The non-transitory machine-readable storagemedium of claim 17, wherein the first data comprises spectrally resolveddata associated with detecting electromagnetic radiation.
 20. Thenon-transitory machine-readable storage medium of claim 17, wherein themodel comprises a physics-based model, and wherein the model fits atemporal evolution of the first data over a duration associated with theprocessing operation.